Overview

Dataset statistics

Number of variables24
Number of observations303
Missing cells338
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory56.9 KiB
Average record size in memory192.4 B

Variable types

Numeric13
Categorical7
DateTime4

Alerts

NOMBRE_CONCEPTO has constant value "CREDITO EDUCATIVO UAO"Constant
OBSERVACIONES has a high cardinality: 182 distinct valuesHigh cardinality
EDAD_CARTERA is highly imbalanced (74.1%)Imbalance
TIPO_GARANTIA is highly imbalanced (77.5%)Imbalance
ESTADO_GARANTIA is highly imbalanced (94.1%)Imbalance
ESTADO_CREDITO is highly imbalanced (55.5%)Imbalance
VALOR_MORA has 292 (96.4%) missing valuesMissing
ESTADO_GARANTIA has 11 (3.6%) missing valuesMissing
OBSERVACIONES has 19 (6.3%) missing valuesMissing
NOMBRE_CONCEPTO has 16 (5.3%) missing valuesMissing
CREDITO has unique valuesUnique
VALOR_AFECTADO has 40 (13.2%) zerosZeros
CUOTAS_PAGADAS has 45 (14.9%) zerosZeros
CUOTAS_PENDIENTES has 246 (81.2%) zerosZeros
CUOTAS_VENCIDAS has 280 (92.4%) zerosZeros
SALDO_CREDITO has 262 (86.5%) zerosZeros
SALDO_VENCIDO has 289 (95.4%) zerosZeros
DIAS_MORA has 278 (91.7%) zerosZeros

Reproduction

Analysis started2023-09-14 23:57:26.625554
Analysis finished2023-09-14 23:57:48.800580
Duration22.18 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

CREDITO
Real number (ℝ)

Distinct303
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144132.9
Minimum105346
Maximum175973
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-09-14T18:57:48.903369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105346
5-th percentile113154.7
Q1133153.5
median144589
Q3157053.5
95-th percentile172666.2
Maximum175973
Range70627
Interquartile range (IQR)23900

Descriptive statistics

Standard deviation17229.379
Coefficient of variation (CV)0.11953814
Kurtosis-0.59023223
Mean144132.9
Median Absolute Deviation (MAD)12179
Skewness-0.28196876
Sum43672268
Variance2.968515 × 108
MonotonicityNot monotonic
2023-09-14T18:57:49.083954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105346 1
 
0.3%
152707 1
 
0.3%
152613 1
 
0.3%
155771 1
 
0.3%
152337 1
 
0.3%
152887 1
 
0.3%
152933 1
 
0.3%
152451 1
 
0.3%
152344 1
 
0.3%
148690 1
 
0.3%
Other values (293) 293
96.7%
ValueCountFrequency (%)
105346 1
0.3%
105362 1
0.3%
105385 1
0.3%
105393 1
0.3%
105591 1
0.3%
106655 1
0.3%
108695 1
0.3%
110546 1
0.3%
110603 1
0.3%
112755 1
0.3%
ValueCountFrequency (%)
175973 1
0.3%
175202 1
0.3%
174817 1
0.3%
174595 1
0.3%
174435 1
0.3%
174375 1
0.3%
174315 1
0.3%
174264 1
0.3%
173911 1
0.3%
173910 1
0.3%

NOMBRE_LINEA
Categorical

Distinct8
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
CREDITO INTERES VENCIDO
157 
PILOS CECILIA MONTALVO DE MORENO
49 
CREDITO PUENTE INTERES VENCIDO
45 
PREGRADO LARGO PLAZO
23 
CREDITO SOY AUTONOMO
21 
Other values (3)
 
8

Length

Max length45
Median length23
Mean length25.290429
Min length20

Characters and Unicode

Total characters7663
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.7%

Sample

1st rowCREDITO INTERES VENCIDO
2nd rowCREDITO INTERES VENCIDO
3rd rowPILOS CECILIA MONTALVO DE MORENO
4th rowPILOS CECILIA MONTALVO DE MORENO
5th rowPILOS CECILIA MONTALVO DE MORENO

Common Values

ValueCountFrequency (%)
CREDITO INTERES VENCIDO 157
51.8%
PILOS CECILIA MONTALVO DE MORENO 49
 
16.2%
CREDITO PUENTE INTERES VENCIDO 45
 
14.9%
PREGRADO LARGO PLAZO 23
 
7.6%
CREDITO SOY AUTONOMO 21
 
6.9%
LIQUIDACION CREDITO LARGO PLAZO 6
 
2.0%
CREDITOS CAI COVID SALDOS ANTERIORES PREGRADO 1
 
0.3%
REFINANCIACION PREGRADO 1
 
0.3%

Length

2023-09-14T18:57:49.254107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-14T18:57:49.404093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
credito 229
21.6%
interes 202
19.1%
vencido 202
19.1%
pilos 49
 
4.6%
cecilia 49
 
4.6%
montalvo 49
 
4.6%
de 49
 
4.6%
moreno 49
 
4.6%
puente 45
 
4.2%
plazo 29
 
2.7%
Other values (11) 108
10.2%

Most occurring characters

ValueCountFrequency (%)
E 1101
14.4%
O 854
11.1%
I 805
10.5%
757
9.9%
N 578
7.5%
R 563
7.3%
T 548
7.2%
C 540
7.0%
D 514
6.7%
S 276
 
3.6%
Other values (11) 1127
14.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6906
90.1%
Space Separator 757
 
9.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1101
15.9%
O 854
12.4%
I 805
11.7%
N 578
8.4%
R 563
8.2%
T 548
7.9%
C 540
7.8%
D 514
7.4%
S 276
 
4.0%
V 252
 
3.6%
Other values (10) 875
12.7%
Space Separator
ValueCountFrequency (%)
757
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6906
90.1%
Common 757
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1101
15.9%
O 854
12.4%
I 805
11.7%
N 578
8.4%
R 563
8.2%
T 548
7.9%
C 540
7.8%
D 514
7.4%
S 276
 
4.0%
V 252
 
3.6%
Other values (10) 875
12.7%
Common
ValueCountFrequency (%)
757
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1101
14.4%
O 854
11.1%
I 805
10.5%
757
9.9%
N 578
7.5%
R 563
7.3%
T 548
7.2%
C 540
7.0%
D 514
6.7%
S 276
 
3.6%
Other values (11) 1127
14.7%
Distinct166
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum2017-12-07 00:00:00
Maximum2023-09-06 00:00:00
2023-09-14T18:57:49.595677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:49.796722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct135
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum2017-12-07 00:00:00
Maximum2023-09-07 00:00:00
2023-09-14T18:57:49.994812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:50.178545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct219
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum2017-12-07 00:00:00
Maximum2023-09-07 00:00:00
2023-09-14T18:57:50.362799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:50.548018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

VALOR
Real number (ℝ)

Distinct118
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4680249.8
Minimum502603
Maximum39833227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-09-14T18:57:50.738124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum502603
5-th percentile1675000
Q12151875
median4341500
Q36282000
95-th percentile7738000
Maximum39833227
Range39330624
Interquartile range (IQR)4130125

Descriptive statistics

Standard deviation3759433.4
Coefficient of variation (CV)0.80325486
Kurtosis49.3309
Mean4680249.8
Median Absolute Deviation (MAD)2161100
Skewness5.7677764
Sum1.4181157 × 109
Variance1.4133339 × 1013
MonotonicityNot monotonic
2023-09-14T18:57:50.937584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5212025 17
 
5.6%
6700000 16
 
5.3%
1675000 13
 
4.3%
1817000 12
 
4.0%
1750500 10
 
3.3%
7738000 10
 
3.3%
7399000 9
 
3.0%
7268000 9
 
3.0%
1934500 8
 
2.6%
1849750 8
 
2.6%
Other values (108) 191
63.0%
ValueCountFrequency (%)
502603 1
 
0.3%
533250 1
 
0.3%
571500 2
 
0.7%
1223792.19 1
 
0.3%
1406400 1
 
0.3%
1453600 1
 
0.3%
1488154 1
 
0.3%
1507500 1
 
0.3%
1599750 1
 
0.3%
1675000 13
4.3%
ValueCountFrequency (%)
39833227 1
 
0.3%
38929709.15 1
 
0.3%
25229642.62 1
 
0.3%
11960393.98 1
 
0.3%
11020100 1
 
0.3%
8683000 5
1.7%
7738000 10
3.3%
7399000 9
3.0%
7268000 9
3.0%
7267920 1
 
0.3%

VALOR_AFECTADO
Real number (ℝ)

Distinct120
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3789646.1
Minimum0
Maximum29211476
Zeros40
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-09-14T18:57:51.128688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11750500
median3562150
Q35779100
95-th percentile7399000
Maximum29211476
Range29211476
Interquartile range (IQR)4028600

Descriptive statistics

Standard deviation3130043.7
Coefficient of variation (CV)0.82594618
Kurtosis19.812631
Mean3789646.1
Median Absolute Deviation (MAD)1887150
Skewness2.820634
Sum1.1482628 × 109
Variance9.7971738 × 1012
MonotonicityNot monotonic
2023-09-14T18:57:51.308971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 40
 
13.2%
5212025 17
 
5.6%
6700000 16
 
5.3%
1750500 10
 
3.3%
7738000 10
 
3.3%
1675000 10
 
3.3%
1817000 9
 
3.0%
7399000 9
 
3.0%
7268000 8
 
2.6%
3000000 8
 
2.6%
Other values (110) 166
54.8%
ValueCountFrequency (%)
0 40
13.2%
31 1
 
0.3%
126 1
 
0.3%
170344 1
 
0.3%
502603 1
 
0.3%
533250 1
 
0.3%
571500 2
 
0.7%
858159 1
 
0.3%
858567 1
 
0.3%
1032926 1
 
0.3%
ValueCountFrequency (%)
29211476 1
 
0.3%
25229643 1
 
0.3%
8683000 2
 
0.7%
7738000 10
3.3%
7399000 9
3.0%
7268000 8
2.6%
7267920 1
 
0.3%
7049000 1
 
0.3%
7002000 6
2.0%
6964200 1
 
0.3%
Distinct122
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum2017-12-07 00:00:00
Maximum2025-05-01 00:00:00
2023-09-14T18:57:51.484186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:51.657794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TOTAL_CUOTAS
Real number (ℝ)

Distinct8
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2475248
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-09-14T18:57:51.791891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile5
Maximum60
Range59
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.2153505
Coefficient of variation (CV)1.9138732
Kurtosis68.969359
Mean3.2475248
Median Absolute Deviation (MAD)0
Skewness7.9243435
Sum984
Variance38.630582
MonotonicityNot monotonic
2023-09-14T18:57:51.924376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 169
55.8%
5 76
25.1%
4 41
 
13.5%
6 8
 
2.6%
2 4
 
1.3%
60 3
 
1.0%
3 1
 
0.3%
32 1
 
0.3%
ValueCountFrequency (%)
1 169
55.8%
2 4
 
1.3%
3 1
 
0.3%
4 41
 
13.5%
5 76
25.1%
6 8
 
2.6%
32 1
 
0.3%
60 3
 
1.0%
ValueCountFrequency (%)
60 3
 
1.0%
32 1
 
0.3%
6 8
 
2.6%
5 76
25.1%
4 41
 
13.5%
3 1
 
0.3%
2 4
 
1.3%
1 169
55.8%

CUOTAS_PAGADAS
Real number (ℝ)

Distinct9
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5577558
Minimum0
Maximum60
Zeros45
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-09-14T18:57:52.057099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q34
95-th percentile5
Maximum60
Range60
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.5217426
Coefficient of variation (CV)1.7678555
Kurtosis108.81909
Mean2.5577558
Median Absolute Deviation (MAD)1
Skewness9.4004086
Sum775
Variance20.446157
MonotonicityNot monotonic
2023-09-14T18:57:52.192196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 136
44.9%
5 60
19.8%
0 45
 
14.9%
4 43
 
14.2%
2 9
 
3.0%
6 7
 
2.3%
44 1
 
0.3%
60 1
 
0.3%
3 1
 
0.3%
ValueCountFrequency (%)
0 45
 
14.9%
1 136
44.9%
2 9
 
3.0%
3 1
 
0.3%
4 43
 
14.2%
5 60
19.8%
6 7
 
2.3%
44 1
 
0.3%
60 1
 
0.3%
ValueCountFrequency (%)
60 1
 
0.3%
44 1
 
0.3%
6 7
 
2.3%
5 60
19.8%
4 43
 
14.2%
3 1
 
0.3%
2 9
 
3.0%
1 136
44.9%
0 45
 
14.9%

CUOTAS_PENDIENTES
Real number (ℝ)

Distinct10
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68976898
Minimum0
Maximum56
Zeros246
Zeros (%)81.2%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-09-14T18:57:52.320553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum56
Range56
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.9048443
Coefficient of variation (CV)5.66109
Kurtosis147.74889
Mean0.68976898
Median Absolute Deviation (MAD)0
Skewness11.435066
Sum209
Variance15.247809
MonotonicityNot monotonic
2023-09-14T18:57:52.448964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 246
81.2%
1 37
 
12.2%
5 7
 
2.3%
3 5
 
1.7%
4 2
 
0.7%
2 2
 
0.7%
6 1
 
0.3%
16 1
 
0.3%
56 1
 
0.3%
32 1
 
0.3%
ValueCountFrequency (%)
0 246
81.2%
1 37
 
12.2%
2 2
 
0.7%
3 5
 
1.7%
4 2
 
0.7%
5 7
 
2.3%
6 1
 
0.3%
16 1
 
0.3%
32 1
 
0.3%
56 1
 
0.3%
ValueCountFrequency (%)
56 1
 
0.3%
32 1
 
0.3%
16 1
 
0.3%
6 1
 
0.3%
5 7
 
2.3%
4 2
 
0.7%
3 5
 
1.7%
2 2
 
0.7%
1 37
 
12.2%
0 246
81.2%

CUOTAS_VENCIDAS
Real number (ℝ)

Distinct6
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14191419
Minimum0
Maximum6
Zeros280
Zeros (%)92.4%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-09-14T18:57:52.599094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.64822143
Coefficient of variation (CV)4.5676999
Kurtosis46.119561
Mean0.14191419
Median Absolute Deviation (MAD)0
Skewness6.3893927
Sum43
Variance0.42019103
MonotonicityNot monotonic
2023-09-14T18:57:52.934878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 280
92.4%
1 15
 
5.0%
2 4
 
1.3%
5 2
 
0.7%
6 1
 
0.3%
4 1
 
0.3%
ValueCountFrequency (%)
0 280
92.4%
1 15
 
5.0%
2 4
 
1.3%
4 1
 
0.3%
5 2
 
0.7%
6 1
 
0.3%
ValueCountFrequency (%)
6 1
 
0.3%
5 2
 
0.7%
4 1
 
0.3%
2 4
 
1.3%
1 15
 
5.0%
0 280
92.4%

SALDO_CREDITO
Real number (ℝ)

Distinct34
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean689621.87
Minimum0
Maximum36333953
Zeros262
Zeros (%)86.5%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-09-14T18:57:53.101246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5087600
Maximum36333953
Range36333953
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2718909.4
Coefficient of variation (CV)3.942609
Kurtosis100.01406
Mean689621.87
Median Absolute Deviation (MAD)0
Skewness8.5167858
Sum2.0895543 × 108
Variance7.3924681 × 1012
MonotonicityNot monotonic
2023-09-14T18:57:53.300542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 262
86.5%
5179300 4
 
1.3%
5087600 3
 
1.0%
4901400 2
 
0.7%
8683000 2
 
0.7%
5416600 2
 
0.7%
3483340.62 1
 
0.3%
530.71 1
 
0.3%
7182999.69 1
 
0.3%
4121394.52 1
 
0.3%
Other values (24) 24
 
7.9%
ValueCountFrequency (%)
0 262
86.5%
530.71 1
 
0.3%
14101.26 1
 
0.3%
1488154 1
 
0.3%
1515790.37 1
 
0.3%
1646656 1
 
0.3%
1719313.7 1
 
0.3%
1849750 1
 
0.3%
1934500 1
 
0.3%
2113525.75 1
 
0.3%
ValueCountFrequency (%)
36333953.02 1
 
0.3%
11960393.98 1
 
0.3%
11020100 1
 
0.3%
10621751.3 1
 
0.3%
8683000 2
0.7%
7182999.69 1
 
0.3%
5416600 2
0.7%
5338288 1
 
0.3%
5179300 4
1.3%
5087600 3
1.0%

SALDO_VENCIDO
Real number (ℝ)

Distinct15
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145674.37
Minimum0
Maximum11020100
Zeros289
Zeros (%)95.4%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-09-14T18:57:53.459365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11020100
Range11020100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation949141.71
Coefficient of variation (CV)6.5155026
Kurtosis82.598342
Mean145674.37
Median Absolute Deviation (MAD)0
Skewness8.5827231
Sum44139333
Variance9.0086998 × 1011
MonotonicityNot monotonic
2023-09-14T18:57:53.616803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 289
95.4%
1488154 1
 
0.3%
4439369 1
 
0.3%
2321400 1
 
0.3%
14101 1
 
0.3%
5338288 1
 
0.3%
8683000 1
 
0.3%
1719314 1
 
0.3%
11020100 1
 
0.3%
531 1
 
0.3%
Other values (5) 5
 
1.7%
ValueCountFrequency (%)
0 289
95.4%
531 1
 
0.3%
14101 1
 
0.3%
861433 1
 
0.3%
878441 1
 
0.3%
1028802 1
 
0.3%
1488154 1
 
0.3%
1719314 1
 
0.3%
2321400 1
 
0.3%
2873200 1
 
0.3%
ValueCountFrequency (%)
11020100 1
0.3%
8683000 1
0.3%
5338288 1
0.3%
4439369 1
0.3%
3473200 1
0.3%
2873200 1
0.3%
2321400 1
0.3%
1719314 1
0.3%
1488154 1
0.3%
1028802 1
0.3%

DIAS_MORA
Real number (ℝ)

Distinct16
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.940594
Minimum0
Maximum2074
Zeros278
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-09-14T18:57:53.770643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile344.9
Maximum2074
Range2074
Interquartile range (IQR)0

Descriptive statistics

Standard deviation335.11936
Coefficient of variation (CV)4.4129146
Kurtosis20.857182
Mean75.940594
Median Absolute Deviation (MAD)0
Skewness4.6489323
Sum23010
Variance112304.98
MonotonicityNot monotonic
2023-09-14T18:57:53.924096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 278
91.7%
34 4
 
1.3%
1528 3
 
1.0%
97 3
 
1.0%
2074 2
 
0.7%
1892 2
 
0.7%
1709 2
 
0.7%
1333 1
 
0.3%
1312 1
 
0.3%
1332 1
 
0.3%
Other values (6) 6
 
2.0%
ValueCountFrequency (%)
0 278
91.7%
3 1
 
0.3%
34 4
 
1.3%
97 3
 
1.0%
164 1
 
0.3%
365 1
 
0.3%
581 1
 
0.3%
643 1
 
0.3%
916 1
 
0.3%
1312 1
 
0.3%
ValueCountFrequency (%)
2074 2
0.7%
1892 2
0.7%
1709 2
0.7%
1528 3
1.0%
1333 1
 
0.3%
1332 1
 
0.3%
1312 1
 
0.3%
916 1
 
0.3%
643 1
 
0.3%
581 1
 
0.3%

VALOR_MORA
Real number (ℝ)

Distinct11
Distinct (%)100.0%
Missing292
Missing (%)96.4%
Infinite0
Infinite (%)0.0%
Mean166164.73
Minimum27003
Maximum318250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-09-14T18:57:54.058595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27003
5-th percentile39691
Q1136515
median149032
Q3223665.5
95-th percentile287528
Maximum318250
Range291247
Interquartile range (IQR)87150.5

Descriptive statistics

Standard deviation86160.684
Coefficient of variation (CV)0.51852572
Kurtosis-0.18399268
Mean166164.73
Median Absolute Deviation (MAD)48619
Skewness0.12088408
Sum1827812
Variance7.4236635 × 109
MonotonicityNot monotonic
2023-09-14T18:57:54.186456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
318250 1
 
0.3%
256806 1
 
0.3%
249680 1
 
0.3%
197651 1
 
0.3%
52379 1
 
0.3%
144369 1
 
0.3%
147833 1
 
0.3%
27003 1
 
0.3%
149032 1
 
0.3%
156148 1
 
0.3%
(Missing) 292
96.4%
ValueCountFrequency (%)
27003 1
0.3%
52379 1
0.3%
128661 1
0.3%
144369 1
0.3%
147833 1
0.3%
149032 1
0.3%
156148 1
0.3%
197651 1
0.3%
249680 1
0.3%
256806 1
0.3%
ValueCountFrequency (%)
318250 1
0.3%
256806 1
0.3%
249680 1
0.3%
197651 1
0.3%
156148 1
0.3%
149032 1
0.3%
147833 1
0.3%
144369 1
0.3%
128661 1
0.3%
52379 1
0.3%

EDAD_CARTERA
Categorical

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
0
278 
MAYOR A 209
 
16
1-60
 
5
61-180
 
4

Length

Max length11
Median length1
Mean length1.6435644
Min length1

Characters and Unicode

Total characters498
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 278
91.7%
MAYOR A 209 16
 
5.3%
1-60 5
 
1.7%
61-180 4
 
1.3%

Length

2023-09-14T18:57:54.331057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-14T18:57:54.456056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 278
83.0%
mayor 16
 
4.8%
a 16
 
4.8%
209 16
 
4.8%
1-60 5
 
1.5%
61-180 4
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 303
60.8%
A 32
 
6.4%
32
 
6.4%
M 16
 
3.2%
Y 16
 
3.2%
O 16
 
3.2%
R 16
 
3.2%
2 16
 
3.2%
9 16
 
3.2%
1 13
 
2.6%
Other values (3) 22
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 361
72.5%
Uppercase Letter 96
 
19.3%
Space Separator 32
 
6.4%
Dash Punctuation 9
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 303
83.9%
2 16
 
4.4%
9 16
 
4.4%
1 13
 
3.6%
6 9
 
2.5%
8 4
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
A 32
33.3%
M 16
16.7%
Y 16
16.7%
O 16
16.7%
R 16
16.7%
Space Separator
ValueCountFrequency (%)
32
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 402
80.7%
Latin 96
 
19.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 303
75.4%
32
 
8.0%
2 16
 
4.0%
9 16
 
4.0%
1 13
 
3.2%
- 9
 
2.2%
6 9
 
2.2%
8 4
 
1.0%
Latin
ValueCountFrequency (%)
A 32
33.3%
M 16
16.7%
Y 16
16.7%
O 16
16.7%
R 16
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 303
60.8%
A 32
 
6.4%
32
 
6.4%
M 16
 
3.2%
Y 16
 
3.2%
O 16
 
3.2%
R 16
 
3.2%
2 16
 
3.2%
9 16
 
3.2%
1 13
 
2.6%
Other values (3) 22
 
4.4%

TIPO_GARANTIA
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
PAGARE
292 
CHEQUE
 
11

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1818
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAGARE
2nd rowPAGARE
3rd rowPAGARE
4th rowPAGARE
5th rowPAGARE

Common Values

ValueCountFrequency (%)
PAGARE 292
96.4%
CHEQUE 11
 
3.6%

Length

2023-09-14T18:57:54.595447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-14T18:57:54.729720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pagare 292
96.4%
cheque 11
 
3.6%

Most occurring characters

ValueCountFrequency (%)
A 584
32.1%
E 314
17.3%
P 292
16.1%
G 292
16.1%
R 292
16.1%
C 11
 
0.6%
H 11
 
0.6%
Q 11
 
0.6%
U 11
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1818
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 584
32.1%
E 314
17.3%
P 292
16.1%
G 292
16.1%
R 292
16.1%
C 11
 
0.6%
H 11
 
0.6%
Q 11
 
0.6%
U 11
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 1818
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 584
32.1%
E 314
17.3%
P 292
16.1%
G 292
16.1%
R 292
16.1%
C 11
 
0.6%
H 11
 
0.6%
Q 11
 
0.6%
U 11
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 584
32.1%
E 314
17.3%
P 292
16.1%
G 292
16.1%
R 292
16.1%
C 11
 
0.6%
H 11
 
0.6%
Q 11
 
0.6%
U 11
 
0.6%

ESTADO_GARANTIA
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.7%
Missing11
Missing (%)3.6%
Memory size2.5 KiB
VALIDO
290 
DEVUELTO
 
2

Length

Max length8
Median length6
Mean length6.0136986
Min length6

Characters and Unicode

Total characters1756
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVALIDO
2nd rowVALIDO
3rd rowVALIDO
4th rowVALIDO
5th rowVALIDO

Common Values

ValueCountFrequency (%)
VALIDO 290
95.7%
DEVUELTO 2
 
0.7%
(Missing) 11
 
3.6%

Length

2023-09-14T18:57:54.903840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-14T18:57:55.065106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
valido 290
99.3%
devuelto 2
 
0.7%

Most occurring characters

ValueCountFrequency (%)
V 292
16.6%
L 292
16.6%
D 292
16.6%
O 292
16.6%
A 290
16.5%
I 290
16.5%
E 4
 
0.2%
U 2
 
0.1%
T 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1756
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
V 292
16.6%
L 292
16.6%
D 292
16.6%
O 292
16.6%
A 290
16.5%
I 290
16.5%
E 4
 
0.2%
U 2
 
0.1%
T 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1756
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
V 292
16.6%
L 292
16.6%
D 292
16.6%
O 292
16.6%
A 290
16.5%
I 290
16.5%
E 4
 
0.2%
U 2
 
0.1%
T 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
V 292
16.6%
L 292
16.6%
D 292
16.6%
O 292
16.6%
A 290
16.5%
I 290
16.5%
E 4
 
0.2%
U 2
 
0.1%
T 2
 
0.1%

ESTADO_CREDITO
Categorical

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
CANCELADO
245 
EN CARTERA
41 
ANULADO
 
16
REFINANCIADO
 
1

Length

Max length12
Median length9
Mean length9.039604
Min length7

Characters and Unicode

Total characters2739
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowCANCELADO
2nd rowCANCELADO
3rd rowCANCELADO
4th rowCANCELADO
5th rowCANCELADO

Common Values

ValueCountFrequency (%)
CANCELADO 245
80.9%
EN CARTERA 41
 
13.5%
ANULADO 16
 
5.3%
REFINANCIADO 1
 
0.3%

Length

2023-09-14T18:57:55.209536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-14T18:57:55.346468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
cancelado 245
71.2%
en 41
 
11.9%
cartera 41
 
11.9%
anulado 16
 
4.7%
refinanciado 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
A 606
22.1%
C 532
19.4%
E 328
12.0%
N 304
11.1%
D 262
9.6%
O 262
9.6%
L 261
9.5%
R 83
 
3.0%
41
 
1.5%
T 41
 
1.5%
Other values (3) 19
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2698
98.5%
Space Separator 41
 
1.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 606
22.5%
C 532
19.7%
E 328
12.2%
N 304
11.3%
D 262
9.7%
O 262
9.7%
L 261
9.7%
R 83
 
3.1%
T 41
 
1.5%
U 16
 
0.6%
Other values (2) 3
 
0.1%
Space Separator
ValueCountFrequency (%)
41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2698
98.5%
Common 41
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 606
22.5%
C 532
19.7%
E 328
12.2%
N 304
11.3%
D 262
9.7%
O 262
9.7%
L 261
9.7%
R 83
 
3.1%
T 41
 
1.5%
U 16
 
0.6%
Other values (2) 3
 
0.1%
Common
ValueCountFrequency (%)
41
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 606
22.1%
C 532
19.4%
E 328
12.0%
N 304
11.1%
D 262
9.6%
O 262
9.6%
L 261
9.5%
R 83
 
3.0%
41
 
1.5%
T 41
 
1.5%
Other values (3) 19
 
0.7%

ID_ESTUDIANTE
Real number (ℝ)

Distinct97
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2187025.3
Minimum2077059
Maximum2236229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-09-14T18:57:55.503041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2077059
5-th percentile2157115.3
Q12175072
median2186315
Q32196270
95-th percentile2226715.1
Maximum2236229
Range159170
Interquartile range (IQR)21198

Descriptive statistics

Standard deviation23627.963
Coefficient of variation (CV)0.010803699
Kurtosis1.1952619
Mean2187025.3
Median Absolute Deviation (MAD)10940
Skewness-0.22713995
Sum6.6266868 × 108
Variance5.5828066 × 108
MonotonicityNot monotonic
2023-09-14T18:57:55.708820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2175375 31
 
10.2%
2186473 17
 
5.6%
2195358 12
 
4.0%
2157230 12
 
4.0%
2196270 9
 
3.0%
2185512 9
 
3.0%
2195271 8
 
2.6%
2195201 8
 
2.6%
2195779 8
 
2.6%
2205660 7
 
2.3%
Other values (87) 182
60.1%
ValueCountFrequency (%)
2077059 1
0.3%
2107132 1
0.3%
2130302 2
0.7%
2136170 1
0.3%
2141083 1
0.3%
2146707 1
0.3%
2146710 1
0.3%
2147614 1
0.3%
2147619 1
0.3%
2147620 1
0.3%
ValueCountFrequency (%)
2236229 1
 
0.3%
2235576 1
 
0.3%
2235575 1
 
0.3%
2235012 1
 
0.3%
2235011 3
1.0%
2232126 2
0.7%
2231800 1
 
0.3%
2231378 1
 
0.3%
2230416 2
0.7%
2230225 1
 
0.3%

OBSERVACIONES
Categorical

HIGH CARDINALITY  MISSING 

Distinct182
Distinct (%)64.1%
Missing19
Missing (%)6.3%
Memory size2.5 KiB
LVPINTO
 
21
CREDITO PAGARE POR CONTINGENCIA MODALIDAD DUAL-. SE INCLUYE ESTAMPILLA SEGUN CORREO.
 
16
CREDITO CON PAGARE
 
9
KJBETANCOURT CREDITO SOLICITADO POR LA WEB
 
8
OE
 
6
Other values (177)
224 

Length

Max length311
Median length146
Mean length57.073944
Min length2

Characters and Unicode

Total characters16209
Distinct characters57
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)52.1%

Sample

1st rowCREDITO POR LA WEB CON PAGARE
2nd rowCREDITO POR LA WEB CON PAGARE
3rd rowCREDITO CON CHEQUES MATRICULA 2018-01
4th rowCREDITO POR LA WEB CON PAGARE
5th rowCREDITO CON PAGARE

Common Values

ValueCountFrequency (%)
LVPINTO 21
 
6.9%
CREDITO PAGARE POR CONTINGENCIA MODALIDAD DUAL-. SE INCLUYE ESTAMPILLA SEGUN CORREO. 16
 
5.3%
CREDITO CON PAGARE 9
 
3.0%
KJBETANCOURT CREDITO SOLICITADO POR LA WEB 8
 
2.6%
OE 6
 
2.0%
KJBETANCOURT 6
 
2.0%
CREDITO POR LA WEB CON PAGARE 5
 
1.7%
KJBETANCOURT CREDITO SOLCITADO POR LA WEB 5
 
1.7%
OE CREDITO CON PAGARE 4
 
1.3%
JRENDON CREDITO SOLICITADO POR CORREO 4
 
1.3%
Other values (172) 200
66.0%
(Missing) 19
 
6.3%

Length

2023-09-14T18:57:55.948883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
credito 226
 
9.2%
por 196
 
7.9%
la 93
 
3.8%
correo 74
 
3.0%
solicitado 74
 
3.0%
web 71
 
2.9%
con 68
 
2.8%
lvpinto 65
 
2.6%
kjbetancourt 64
 
2.6%
de 57
 
2.3%
Other values (405) 1481
60.0%

Most occurring characters

ValueCountFrequency (%)
2194
13.5%
O 1584
 
9.8%
E 1371
 
8.5%
A 1294
 
8.0%
R 1139
 
7.0%
I 934
 
5.8%
C 908
 
5.6%
T 874
 
5.4%
D 777
 
4.8%
L 683
 
4.2%
Other values (47) 4451
27.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12890
79.5%
Space Separator 2194
 
13.5%
Decimal Number 673
 
4.2%
Other Punctuation 332
 
2.0%
Dash Punctuation 86
 
0.5%
Currency Symbol 13
 
0.1%
Control 9
 
0.1%
Open Punctuation 6
 
< 0.1%
Close Punctuation 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1584
12.3%
E 1371
10.6%
A 1294
10.0%
R 1139
8.8%
I 934
 
7.2%
C 908
 
7.0%
T 874
 
6.8%
D 777
 
6.0%
L 683
 
5.3%
N 674
 
5.2%
Other values (22) 2652
20.6%
Decimal Number
ValueCountFrequency (%)
0 214
31.8%
2 136
20.2%
1 91
13.5%
3 62
 
9.2%
7 56
 
8.3%
9 28
 
4.2%
5 26
 
3.9%
8 24
 
3.6%
4 21
 
3.1%
6 15
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 135
40.7%
/ 124
37.3%
% 45
 
13.6%
, 14
 
4.2%
# 7
 
2.1%
: 3
 
0.9%
¿ 2
 
0.6%
; 2
 
0.6%
Control
ValueCountFrequency (%)
8
88.9%
1
 
11.1%
Space Separator
ValueCountFrequency (%)
2194
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12890
79.5%
Common 3319
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1584
12.3%
E 1371
10.6%
A 1294
10.0%
R 1139
8.8%
I 934
 
7.2%
C 908
 
7.0%
T 874
 
6.8%
D 777
 
6.0%
L 683
 
5.3%
N 674
 
5.2%
Other values (22) 2652
20.6%
Common
ValueCountFrequency (%)
2194
66.1%
0 214
 
6.4%
2 136
 
4.1%
. 135
 
4.1%
/ 124
 
3.7%
1 91
 
2.7%
- 86
 
2.6%
3 62
 
1.9%
7 56
 
1.7%
% 45
 
1.4%
Other values (15) 176
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16177
99.8%
None 32
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2194
13.6%
O 1584
 
9.8%
E 1371
 
8.5%
A 1294
 
8.0%
R 1139
 
7.0%
I 934
 
5.8%
C 908
 
5.6%
T 874
 
5.4%
D 777
 
4.8%
L 683
 
4.2%
Other values (40) 4419
27.3%
None
ValueCountFrequency (%)
É 14
43.8%
Í 8
25.0%
Ú 3
 
9.4%
¿ 2
 
6.2%
Ñ 2
 
6.2%
Á 2
 
6.2%
Ó 1
 
3.1%

PERIODO
Real number (ℝ)

Distinct21
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202040.76
Minimum201801
Maximum202303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-09-14T18:57:56.123867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201801
5-th percentile201801
Q1201907
median202008
Q3202201
95-th percentile202303
Maximum202303
Range502
Interquartile range (IQR)294

Descriptive statistics

Standard deviation159.59427
Coefficient of variation (CV)0.00078991127
Kurtosis-1.0666686
Mean202040.76
Median Absolute Deviation (MAD)107
Skewness0.13256939
Sum61218349
Variance25470.331
MonotonicityNot monotonic
2023-09-14T18:57:56.294020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
202008 30
9.9%
202201 26
 
8.6%
201907 25
 
8.3%
201801 24
 
7.9%
202001 23
 
7.6%
201807 22
 
7.3%
202203 22
 
7.3%
202101 22
 
7.3%
202301 21
 
6.9%
202103 21
 
6.9%
Other values (11) 67
22.1%
ValueCountFrequency (%)
201801 24
7.9%
201807 22
7.3%
201901 20
6.6%
201902 1
 
0.3%
201903 1
 
0.3%
201907 25
8.3%
201908 1
 
0.3%
201910 1
 
0.3%
201911 1
 
0.3%
201912 1
 
0.3%
ValueCountFrequency (%)
202303 18
5.9%
202301 21
6.9%
202203 22
7.3%
202201 26
8.6%
202103 21
6.9%
202101 22
7.3%
202008 30
9.9%
202007 1
 
0.3%
202005 21
6.9%
202003 1
 
0.3%

NOMBRE_CONCEPTO
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing16
Missing (%)5.3%
Memory size2.5 KiB
CREDITO EDUCATIVO UAO
287 

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters6027
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCREDITO EDUCATIVO UAO
2nd rowCREDITO EDUCATIVO UAO
3rd rowCREDITO EDUCATIVO UAO
4th rowCREDITO EDUCATIVO UAO
5th rowCREDITO EDUCATIVO UAO

Common Values

ValueCountFrequency (%)
CREDITO EDUCATIVO UAO 287
94.7%
(Missing) 16
 
5.3%

Length

2023-09-14T18:57:56.452370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-14T18:57:56.578476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
credito 287
33.3%
educativo 287
33.3%
uao 287
33.3%

Most occurring characters

ValueCountFrequency (%)
O 861
14.3%
C 574
9.5%
E 574
9.5%
D 574
9.5%
I 574
9.5%
T 574
9.5%
574
9.5%
U 574
9.5%
A 574
9.5%
R 287
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5453
90.5%
Space Separator 574
 
9.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 861
15.8%
C 574
10.5%
E 574
10.5%
D 574
10.5%
I 574
10.5%
T 574
10.5%
U 574
10.5%
A 574
10.5%
R 287
 
5.3%
V 287
 
5.3%
Space Separator
ValueCountFrequency (%)
574
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5453
90.5%
Common 574
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 861
15.8%
C 574
10.5%
E 574
10.5%
D 574
10.5%
I 574
10.5%
T 574
10.5%
U 574
10.5%
A 574
10.5%
R 287
 
5.3%
V 287
 
5.3%
Common
ValueCountFrequency (%)
574
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 861
14.3%
C 574
9.5%
E 574
9.5%
D 574
9.5%
I 574
9.5%
T 574
9.5%
574
9.5%
U 574
9.5%
A 574
9.5%
R 287
 
4.8%

Interactions

2023-09-14T18:57:46.223136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:27.252632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-09-14T18:57:30.524536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:32.285027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:33.819363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-09-14T18:57:36.857155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:38.388589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:40.101711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:41.635160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:43.186433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:44.608452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:46.338682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:27.369241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-09-14T18:57:32.407200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:33.936875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:35.470769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-09-14T18:57:43.291368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:44.735374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:46.466568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:27.495788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:29.146654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:30.782240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:32.537547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:34.066208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:35.598489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-09-14T18:57:38.641276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:40.343681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:41.909003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:43.412059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:44.873639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:46.583933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:27.619775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:29.267814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:30.908346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:32.657926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:34.184896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:35.717307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:37.219483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:38.765617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:40.460940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:42.026107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:43.531939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:45.005949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:46.700558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:27.749799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:29.390613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:31.035450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:32.774953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:34.301458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:35.832717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:37.336120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:38.885345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:40.576742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:42.141127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:43.643066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:45.128905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:46.815578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:27.883074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:29.512999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:31.159947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:32.892059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:34.415552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:35.947488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:37.450631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:39.005825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:40.699255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:42.263669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:43.752795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:45.254332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:47.094450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:28.012910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:29.632400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:31.276901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:33.003595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:34.529256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:36.055716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:37.563940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:39.120556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:40.812389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:42.378098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:43.857927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:45.373780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:47.208652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:28.149136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:29.761179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:31.397488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:33.120652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:34.646969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:36.170622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:37.684403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:39.398912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:40.927830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:42.498258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:43.959377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:45.498836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:47.331620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:28.284530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:29.896218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:31.522992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:33.243359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:34.773409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:36.293146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:37.817127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:39.521994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:41.051424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:42.619742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:44.060915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:45.628995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:47.444481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:28.401741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:30.019230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:31.795159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:33.358789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:34.893171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:36.406816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:37.934878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:39.638936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:41.164635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:42.733718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:44.163436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:45.751197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:47.550961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:28.507006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:30.137472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:31.904209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:33.464069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:35.000540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:36.510900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:38.041973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:39.748564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:41.271293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:42.833842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:44.282471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:45.865014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:47.664133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:28.613812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:30.262285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:32.018638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:33.574064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:35.108456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:36.615990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:38.144521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:39.851675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:41.374578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:42.949476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:44.385260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:45.968667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:47.805117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:28.742728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:30.403655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:32.169149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:33.703564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:35.239758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:36.743680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:38.275842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:39.983932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:41.504478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:43.075670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:44.487474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-14T18:57:46.101258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-09-14T18:57:48.027617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-14T18:57:48.455204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-14T18:57:48.696597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CREDITONOMBRE_LINEAFECHA_SOLICITUDFECHA_APROBACIONFECHA_MOVIMIENTOVALORVALOR_AFECTADOFECHA_PROXIMO_VENCIMIENTOTOTAL_CUOTASCUOTAS_PAGADASCUOTAS_PENDIENTESCUOTAS_VENCIDASSALDO_CREDITOSALDO_VENCIDODIAS_MORAVALOR_MORAEDAD_CARTERATIPO_GARANTIAESTADO_GARANTIAESTADO_CREDITOID_ESTUDIANTEOBSERVACIONESPERIODONOMBRE_CONCEPTO
0105346CREDITO INTERES VENCIDO2017-12-072018-01-022018-05-02 00:11:366700000.067000002018-01-0244000.000NaN0PAGAREVALIDOCANCELADO2167030CREDITO POR LA WEB CON PAGARE201801CREDITO EDUCATIVO UAO
1108695CREDITO INTERES VENCIDO2017-12-112018-01-022018-06-02 09:33:163400000.034000002018-01-0244000.000NaN0PAGAREVALIDOCANCELADO2167027CREDITO POR LA WEB CON PAGARE201801CREDITO EDUCATIVO UAO
2105385PILOS CECILIA MONTALVO DE MORENO2017-12-072017-12-072017-12-07 00:00:001675000.016750002017-12-0711000.000NaN0PAGAREVALIDOCANCELADO2157161NaN201801CREDITO EDUCATIVO UAO
3105393PILOS CECILIA MONTALVO DE MORENO2017-12-072017-12-072017-12-07 00:00:001675000.016750002017-12-0711000.000NaN0PAGAREVALIDOCANCELADO2157255NaN201801CREDITO EDUCATIVO UAO
4106655PILOS CECILIA MONTALVO DE MORENO2017-12-112017-12-112017-12-11 00:00:001507500.015075002017-12-1111000.000NaN0PAGAREVALIDOCANCELADO2157230NaN201801CREDITO EDUCATIVO UAO
5105591PILOS CECILIA MONTALVO DE MORENO2017-12-072017-12-072017-12-07 00:00:001675000.016750002017-12-0711000.000NaN0PAGAREVALIDOCANCELADO2167040NaN201801CREDITO EDUCATIVO UAO
6105362PILOS CECILIA MONTALVO DE MORENO2017-12-072017-12-072017-12-07 00:00:001675000.016750002017-12-0711000.000NaN0PAGAREVALIDOCANCELADO2157111NaN201801CREDITO EDUCATIVO UAO
7110546PREGRADO LARGO PLAZO2017-12-122017-12-122017-12-12 00:00:002010000.020100002017-12-1211000.000NaN0PAGAREVALIDOCANCELADO2157166NaN201801CREDITO EDUCATIVO UAO
8113350PILOS CECILIA MONTALVO DE MORENO2018-01-102018-01-102018-01-10 00:00:001675000.016750002018-01-1011000.000NaN0PAGAREVALIDOCANCELADO2167365NaN201801CREDITO EDUCATIVO UAO
9113395CREDITO INTERES VENCIDO2018-01-102018-01-022018-05-02 00:00:006700000.067000002018-01-0244000.002074318250.0MAYOR A 209CHEQUENaNCANCELADO2147620CREDITO CON CHEQUES MATRICULA 2018-01201801CREDITO EDUCATIVO UAO
CREDITONOMBRE_LINEAFECHA_SOLICITUDFECHA_APROBACIONFECHA_MOVIMIENTOVALORVALOR_AFECTADOFECHA_PROXIMO_VENCIMIENTOTOTAL_CUOTASCUOTAS_PAGADASCUOTAS_PENDIENTESCUOTAS_VENCIDASSALDO_CREDITOSALDO_VENCIDODIAS_MORAVALOR_MORAEDAD_CARTERATIPO_GARANTIAESTADO_GARANTIAESTADO_CREDITOID_ESTUDIANTEOBSERVACIONESPERIODONOMBRE_CONCEPTO
293173720CREDITO INTERES VENCIDO2023-07-112023-07-17 00:10:082023-08-10 12:47:314341500.008581592023-08-0450523483340.6287844134NaN1-60PAGAREVALIDOEN CARTERA2230416HDALEGRÍA CRÉDITO SOLICITADO POR EL PORTAL. // PDTE. GARANTÍAS.// 17/07/2023 - GARANTIAS ASOCIADAS 2023-03 - DCVASQUEZ // BGNAVIA ASOCIA202303CREDITO EDUCATIVO UAO
294172851CREDITO INTERES VENCIDO2023-06-272023-07-04 00:00:002023-09-04 23:20:033524500.0014109742023-10-0452302113525.7900NaN0PAGAREVALIDOEN CARTERA2235575JRENDON CREDITO SOLICITAD POR CORREO, CONFIRMADO POR TE // GARANTIAS ASOCIADAS 2023-03 JCJIMENEZH //202303CREDITO EDUCATIVO UAO
295173751CREDITO INTERES VENCIDO2023-07-112023-07-12 00:10:062023-07-12 00:10:067049000.0002023-07-1250510.0000NaN0PAGAREVALIDOANULADO2235011JRENDON CREDITO SOLICITADO POR CORREO Y CONFIRMADO POR TEL//LVPINTO SE ANULA CREDITO DADO QUE TOMARA PUENTE SEGUN CHAT DE JRENDON202303NaN
296174595CREDITO INTERES VENCIDO2023-07-192023-07-19 00:00:002023-09-06 10:40:004338292.0021713152023-10-0442202166977.1700NaN0PAGAREVALIDOEN CARTERA2232126KJBETANCOURT CREDITO SOLCITADO POR CORREO202303CREDITO EDUCATIVO UAO
297174315CREDITO INTERES VENCIDO2023-07-172023-07-25 00:10:092023-09-06 11:20:023524500.0014109742023-10-0452302113525.7500NaN0PAGAREVALIDOEN CARTERA2236229JRENDON PDT CONFIRMAR VALOR, NUMERO DE CUOTAS Y GARANTIAS // GARANTIAS ASOCIADAS 2023-03 JCJIMENEZH // JRENDON CREDITO SOLICITADO POR CORREO202303CREDITO EDUCATIVO UAO
298175202CREDITO INTERES VENCIDO2023-07-312023-07-31 00:00:002023-07-31 00:00:008683000.0002023-08-0450528683000.00347320034NaN1-60PAGAREVALIDOEN CARTERA2205660JRENDON CREDITO SOLICITADO POR CORREO/ACUERDO DE PAGO/ AUTORIZADO POR JHIDARRAGA.202303CREDITO EDUCATIVO UAO
299175973REFINANCIACION PREGRADO2023-09-062023-09-07 00:00:002023-09-07 00:00:0011960393.9802023-09-3032032011960393.9800NaN0PAGAREVALIDOEN CARTERA2157166LVPINTO CASO AUTORIZADO POR COMITE EL DIA 06/09/2023 CON LA PROPUESTA PLANTEADA POR LA FUNACIONARIA DIANA MARICEL RODRIGUEZ SEGUN CORREO. SIN EMBARGO POR INSTRUCCIONES DEL COMITE SE DEBE AUMENTAR $100.000 A LA PRIMERA CUOTA EXTRA QUEDANDO EN $1.000.000202303CREDITO EDUCATIVO UAO
300173340CREDITO INTERES VENCIDO2023-07-052023-07-07 00:00:002023-08-03 10:15:374300000.008585672023-08-0450523441433.3986143334NaN1-60PAGAREVALIDOEN CARTERA2225451KJBETANCOURT CREDITO SOCLITADO POR LA WEB202303CREDITO EDUCATIVO UAO
301173910CREDITO INTERES VENCIDO2023-07-132023-07-13 00:00:002023-07-13 00:00:003524500.0002023-11-0320203524500.0000NaN0PAGAREVALIDOEN CARTERA2235011JRENDON CREDITO SOLICITADO POR CORREO, CONFIRMADO POR TEL CON LA ESTUDIANTE. BECA ADMINISTRACIÓN DUAL202303CREDITO EDUCATIVO UAO
302174817CREDITO INTERES VENCIDO2023-07-242023-07-24 00:00:002023-09-02 14:20:055000000.0020013272023-10-0452302998673.2400NaN0PAGAREVALIDOEN CARTERA2225960KJBETANCOURT CREDITO SOLCITADO POR LA WEB PDTE CONFIRMAR VALOR POR QUE INDICA TIENE UN DESCTO// BGNAVIA ASOCIA202303CREDITO EDUCATIVO UAO